To quantify the uncertainty in numerical weather prediction (NWP) forecasts, ensemble prediction systems are utilized. Although NWP forecasts continuously improve, they suffer from systematic bias and dispersion errors. To obtain well calibrated and sharp predictive probability distributions, statistical postprocessing methods are applied to NWP output. Recent developments focus on multivariate postprocessing models incorporating dependencies directly into the model. We introduce three novel bivariate postprocessing approaches, and analyze their performance for joint postprocessing of bivariate wind vector components for 60 stations in Germany. Bivariate vine copula based models, a bivariate gradient boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared to bivariate EMOS. The case study indicates that the novel bivariate methods improve over the bivariate EMOS approaches. The bivariate DRN and the most flexible version of the bivariate vine copula approach exhibit the best performance in terms of verification scores and calibration.

翻译：为量化数值天气预报（NWP）预测中的不确定性，通常采用集合预报系统。尽管NWP预测持续改进，但仍存在系统性偏差和离散误差。为获得校准良好且锐利的预测概率分布，需对NWP输出结果应用统计后处理方法。最新研究进展聚焦于将依赖关系直接纳入模型的多变量后处理模型。本文提出三种新型双变量后处理方法，并分析其在德国60个站点对双变量风矢量分量进行联合后处理的性能。研究将基于双变量藤Copula的模型、集合模型输出统计（EMOS）的双变量梯度提升版本，以及双变量分布回归网络（DRN）与双变量EMOS方法进行对比。案例研究表明，新型双变量方法较双变量EMOS方法有所改进。在验证评分和校准度方面，双变量DRN与最具灵活性的双变量藤Copula方法展现出最优性能。